Exam 2

Question 1

Hypothesis

Answer 1

a theory about how the world works

• • proposed as an explanation for data
• • posed as statement about population parameters

Question 2

Hypothesis testing

Answer 2

a method that used inferential statistics which of two hypothesis data support

Question 3

likelihood

Answer 3

probability distribution of a statistic, according to each hypothesis
–if result is likely according to a hypothesis, we say data “support” or “are consistent with” the hypothesis

Question 4

binary data

Answer 4

a set of two-choice outcomes

yes/no

Question 5

binomial variable

Answer 5

a statistic for binary samples

– frequency of “yes” or “no”

Question 6

binomial distribution

Answer 6

probability distribution for a binomial variables

Question 7

null hypothesis

Answer 7

nothing interesting going on, blind chance

Question 8

alternative hypothesis

Answer 8

one outcome more likely than expexed by chance

Question 9

critical value

Answer 9

value or statistic must exceed to reject null hypothesis (luck)

Question 10

sign test

Answer 10

ignore magnitude of change; just direction

–same logic as other binomial tests

Question 11

Type 1 error

Answer 11

null hypothesis is true, but we reject it

– conclude a useless treatment is effective

Question 12

Type II error

Answer 12

null hypothesis is false, but we don’t reject it

– don’t recognize when a treatment is effective

Question 13

Type I error rate

Answer 13

proportion of times, when null hypothesis is true, that we mistakenly reject it
—Fraction of bogus treatments that we conclude are effective

Question 14

Alpha Level

Answer 14

Chosen type I error rate

• -Usually .05 in Psychology
• -Determines critical value

Question 15

Replication

Answer 15

• -doing exactly the same experiment but with a new sample

- -sampling variability means each replication will result in different value of statistic

Question 16

Sampliing distribution

Answer 16

the probability distribution of some statistic over repeated replication of an experiment

Question 17

distribution of sample means

Answer 17

the probability distribution for M

Question 18

Standard Error

Answer 18

Typical distance from M to MEW

Question 19

Law of Large Numbers

Answer 19

The larger the sample the closer M will be to MEW

Formally : as n goes to infinity, SE goes to 0

Question 20

Central Limit Theorum

Answer 20

Characterized distribution of sample mean

–works for any population distribution

Question 21

Distribution of sample variances

Answer 21

Probability distribution for s over repeated replication

Question 22

Chi-Square

Answer 22

probability distribution for sampel variance

–positive skew; variance sensitive to outliers

Question 23

t statistic

Answer 23

deviation of sample means divided by estimated standard error

Question 24

t distribution

Answer 24

sampling distribution of t statistic

–derived from ration of Normal and modified x (squared)

Question 25

t-test

Answer 25

Steps of t-test
1. State clearly the two hypotheses
2. Determine null and alternative hypotheses
H0: µ = µ0
H1: µ ≠ µ0
3. Compute the test statistic t from the data
t = M −µ0
s n
4. Determine likelihood function for test statistic according to H0
t distribution with n-1 degrees of freedom
5. Find critical value
R: qt(alpha,n-1,lower.tail=FALSE)
6. Compare actual result to critical value
t < tcrit: Retain null hypothesis, µ = µ0
t > tcrit: Reject null hypothesis, µ ≠ µ0

Question 26

degrees of freedom

Answer 26

df = n -1

Question 27

test statistic

Answer 27

statistic computed from sample to decide between hypotheses

–relevant to hypotheses being tested

Question 28

critical region

Answer 28

Range of value that will lead to rejecting null hypothesis

– all values beyond critical value

Question 29

Type II error rate

Answer 29

If the null is false, probability of failing to reject it

–depends on how false the null is

Question 30

p-value

Answer 30

probability of getting a value equal to or more extreme than what you actually
–cumulative distribution or quantile within sampling distribution

Question 31

Independent samples t-test

Answer 31

often interested in whether two groups have same mean

Question 32

Mean Squares

Answer 32

Average of squared deviations

–Used for estimating variance population

Question 33

Paired-Samples T-test

Answer 33

Data are pairs of scores (Xa, Xb)
–Form two samples, Xa and Xb
–Samples are not independent
Same null hypothesis as with independent samples

Approach
–Compute difference scores, Xdiff = Xa-Xb

Question 34

Difference score (For paired samples t test)

Answer 34

Xdiff = Xa - Xb

Question 35

Effect Size

Answer 35

if there is an effect, how big is it?

—How different is mew from mew not or mew A from mew b etc

Question 36

Point Estimate

Answer 36

We don’t know exact effect size; samples just provide an estimate

Question 37

Standardized Effect Size

Answer 37

Interpreting effect size depends on variable being measured
—Improving digit span by 2 more important than IQ
Solution: measure effect size relative to variability in raw scores